4.8 Article

A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST

期刊

NATURE COMMUNICATIONS
卷 14, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41467-023-38539-w

关键词

-

向作者/读者索取更多资源

Researchers developed a deep learning-based method called REST for denoising and restoring information in cryo-electron tomography. The method performed well in both simulated and real datasets and was able to reveal the dynamic states of target molecules.
Cryo-electron tomography (cryo-ET) is widely used to explore the 3D density of biomacromolecules. However, the heavy noise and missing wedge effect prevent directly visualizing and analyzing the 3D reconstructions. Here, we introduced REST, a deep learning strategy-based method to establish the relationship between low-quality and high-quality density and transfer the knowledge to restore signals in cryo-ET. Test results on the simulated and real cryo-ET datasets show that REST performs well in denoising and compensating the missing wedge information. The application in dynamic nucleosomes, presenting either in the form of individual particles or in the context of cryo-FIB nuclei section, indicates that REST has the capability to reveal different conformations of target macromolecules without subtomogram averaging. Moreover, REST noticeably improves the reliability of particle picking. These advantages enable REST to be a powerful tool for the straightforward interpretation of target macromolecules by visual inspection of the density and of a broad range of other applications in cryo-ET, such as segmentation, particle picking, and subtomogram averaging. Heavy noise and missing wedge effect hamper the efficient visualization and analysis in cryo-ET. Here, authors present a deep learning-based method for directly visualizing and revealing the dynamic states of target molecules.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据